Biosensor systems provide an analysis of a biological fluid sample, such as blood, serum, plasma, urine, saliva, interstitial, or intracellular fluid. Typically, the systems include a measurement device that analyzes a sample residing in a test sensor. The sample usually is in liquid form and in addition to being a biological fluid, may be the derivative of a biological fluid, such as an extract, a dilution, a filtrate, or a reconstituted precipitate. The analysis performed by the biosensor system determines the presence and/or concentration of one or more analytes, such as alcohol, glucose, uric acid, lactate, cholesterol, bilirubin, free fatty acids, triglycerides, proteins, ketones, phenylalanine or enzymes, in the biological fluid. The analysis may be useful in the diagnosis and treatment of physiological abnormalities. For example, a person with diabetes may use a biosensor system to determine the glucose level in blood for adjustments to diet and/or medication.
In blood samples including hemoglobin (Hb), the presence and/or concentration of total hemoglobin and glycated hemoglobin (HbA1c) may be determined. HbA1c (%-A1c) is a reflection of the state of glucose control in diabetic patients, providing insight into the average glucose control over the three months preceding the test. For diabetic individuals, an accurate measurement of %-A1c assists in the determination of the blood glucose level, as adjustments to diet and/or medication are based on these levels.
Biosensor systems may be designed to analyze one or more analytes and may use different volumes of biological fluids. Some systems may analyze a single drop of blood, such as from 0.25-15 microliters (μL) in volume. Biosensor systems may be implemented using bench-top, portable, and like measurement devices. Portable measurement devices may be hand-held and allow for the identification and/or quantification of one or more analytes in a sample. Examples of portable measurement systems include the Elite® meters of Bayer HealthCare in Tarrytown, N.Y., while examples of bench-top measurement systems include the Electrochemical Workstation available from CH Instruments in Austin, Tex.
Biosensor systems may use optical and/or electrochemical methods to analyze the biological fluid. In some optical systems, the analyte concentration is determined by measuring light that has interacted with or been absorbed by a light-identifiable species, such as the analyte or a reaction or product formed from a chemical indicator reacting with the analyte. In other optical systems, a chemical indicator fluoresces or emits light in response to the analyte when illuminated by an excitation beam. The light may be converted into an electrical output signal, such as current or potential, which may be similarly processed to the output signal from an electrochemical system. In either optical system, the system measures and correlates the light with the analyte concentration of the sample.
In light-absorption optical systems, the chemical indicator produces a reaction product that absorbs light. A chemical indicator such as tetrazolium along with an enzyme such as diaphorase may be used. Tetrazolium usually forms formazan (a chromagen) in response to the redox reaction of the analyte. An incident input beam from a light source is directed toward the sample. The light source may be a laser, a light emitting diode, or the like. The incident beam may have a wavelength selected for absorption by the reaction product. As the incident beam passes through the sample, the reaction product absorbs a portion of the incident beam, thus attenuating or reducing the intensity of the incident beam. The incident beam may be reflected back from or transmitted through the sample to a detector. The detector collects and measures the attenuated incident beam (output signal). The amount of light attenuated by the reaction product is an indication of the analyte concentration in the sample.
In light-generated optical systems, the chemical detector fluoresces or emits light in response to the analyte redox reaction. A detector collects and measures the generated light (output signal). The amount of light produced by the chemical indicator is an indication of the analyte concentration in the sample.
An example of an optical system using reflectance is a laminar flow A1c system that determines the concentration of A1c hemoglobin in blood. These systems use immunoassay chemistry where the blood is introduced to the test sensor where it reacts with reagents and then flows along a reagent membrane. When contacted by the blood, A1c antibody coated color beads release and move along with the blood sample to a detection zone 1. Because of the competition between the A1c in the blood sample and an A1c peptide present in detection zone 1 for the color beads, color beads not attached to the A1c antibody are captured at zone 1 and are thus detected as the A1c signal from the change in reflectance. The total hemoglobin (THb) in the blood sample also is reacting with other blood treatment reagents and moves downstream into detection zone 2, where it is measured at a different wavelength. For determining the concentration of A1c in the blood sample, the reflectance signal is proportional to the A1c analyte concentration (%-A1c). For the THb measurement, however, the reflectance in zone 2 is inversely proportional to the THb (mg/dL) for the detection system.
In electrochemical systems, the analyte concentration is determined from an electrical signal generated by an oxidation/reduction or redox reaction of the analyte or a species responsive to the analyte when an input signal is applied to the sample. The input signal may be a potential or current and may be constant, variable, or a combination thereof such as when an AC signal is applied with a DC signal offset. The input signal may be applied as a single pulse or in multiple pulses, sequences, or cycles. An enzyme or similar species may be added to the sample to enhance the electron transfer from a first species to a second species during the redox reaction. The enzyme or similar species may react with a single analyte, thus providing specificity to a portion of the generated output signal. A mediator may be used to maintain the oxidation state of the enzyme and/or assist with electron transfer from the analyte to an electrode.
Electrochemical biosensor systems usually include a measurement device having electrical contacts that connect with the electrical conductors of the test sensor. The conductors may be made from conductive materials, such as solid metals, metal pastes, conductive carbon, conductive carbon pastes, conductive polymers, and the like. The electrical conductors typically connect to working, counter, reference, and/or other electrodes that extend into a sample reservoir. One or more electrical conductors also may extend into the sample reservoir to provide functionality not provided by the electrodes.
The measurement device applies an input signal through the electrical contacts to the electrical conductors of the test sensor. The electrical conductors convey the input signal through the electrodes into the sample present in the sample reservoir. The redox reaction of the analyte generates an electrical output signal in response to the input signal. The electrical output signal from the test sensor may be a current (as generated by amperometry or voltammetry), a potential (as generated by potentiometry/galvanometry), or an accumulated charge (as generated by coulometry). The measurement device may have the processing capability to measure and correlate the output signal with the presence and/or concentration of one or more analytes in the sample.
In coulometry, a potential is applied to the sample to exhaustively oxidize or reduce the analyte. A biosensor system using coulometry is described in U.S. Pat. No. 6,120,676. In amperometry, an electrical signal of constant potential (voltage) is applied to the electrical conductors of the test sensor while the measured output signal is a current. Biosensor systems using amperometry are described in U.S. Pat. Nos. 5,620,579; 5,653,863; 6,153,069; and 6,413,411. In voltammetry, an electric signal of varying potential is applied to a sample of biological fluid, while the measured output is current. In gated amperometry and gated voltammetry, pulsed inputs are used as described in WO 2007/013915 and WO 2007/040913, respectively.
Output signal values that are responsive to the analyte concentration of the sample include those obtained from the analytic input signal. Output signal values that are substantially independent of values responsive to the analyte concentration of the sample include values responsive to temperature and values substantially responsive to interferents, such as the hematocrit or acetaminophen content of a blood sample when the analyte is glucose, for example. Output signals substantially not responsive to analyte concentration may be referred to as secondary output signals, as they are not primary output signals responsive to the alteration of light by the analyte or analyte responsive indicator, electrochemical redox reaction of the analyte, or analyte responsive redox mediator. Secondary output signals may arise from the sample or from other sources, such as a thermocouple.
In many biosensor systems, the test sensor may be adapted for use outside, inside, or partially inside a living organism. When used outside a living organism, a sample of the biological fluid may be introduced into a sample reservoir in the test sensor. The test sensor may be placed in the measurement device before, after, or during the introduction of the sample for analysis. When inside or partially inside a living organism, the test sensor may be continually immersed in the sample or the sample may be intermittently introduced to the test sensor. The test sensor may include a reservoir that partially isolates a volume of the sample or be open to the sample. When open, the test sensor may take the form of a fiber or other structure placed in contact with the biological fluid. Similarly, the sample may continuously flow through the test sensor, such as for continuous monitoring, or be interrupted, such as for intermittent monitoring, for analysis.
The measurement performance of a biosensor system is defined in terms of accuracy and precision. Accuracy reflects the combined effects of random and systematic error components. Systematic error, or trueness, is the difference between the average value determined from the biosensor system and one or more accepted reference values for the analyte concentration of the biological fluid. Trueness may be expressed in terms of mean bias, with larger mean bias values representing lower trueness and thereby contributing to less accuracy. Precision is the closeness of agreement among multiple analyte readings in relation to a mean. One or more error in the analysis contribute to the bias and/or imprecision of the analyte concentration determined by the biosensor system. A reduction in the analysis error of a biosensor system therefore leads to an increase in accuracy and thus an improvement in measurement performance.
Bias may be expressed in terms of “absolute bias” or “percent bias”. Absolute bias is the difference between the determined concentration and the reference concentration, and may be expressed in the units of the measurement, such as mg/dL, while percent bias may be expressed as a percentage of the absolute bias value over 100 mg/dL or the reference analyte concentration of the sample. For glucose concentrations less than 100 mg/dL, percent bias is defined as (the absolute bias over 100 mg/dL)*100. For glucose concentrations of 100 mg/dL and higher, percent bias is defined as the absolute bias over the reference analyte concentration*100. Accepted reference values for the analyte glucose in blood samples may be obtained with a reference instrument, such as the YSI 2300 STAT PLUS™ available from YSI Inc., Yellow Springs, Ohio. Other reference instruments and ways to determine percent bias may be used for other analytes. For the %-A1c measurements, the error may be expressed as either absolute bias or percent bias against the %-A1c reference value for the therapeutic range of 4-12%. Accepted reference values for the %-A1c in blood samples may be obtained with a reference instrument, such as the Tosoh G7 instrument available from Tosoh Corp, Japan.
Hematocrit bias refers to the average difference (systematic error) between the reference glucose concentration obtained with a reference instrument and experimental glucose readings obtained from a biosensor system for samples containing differing hematocrit levels. The difference between the reference and values obtained from the system results from the varying hematocrit level between specific blood samples and may be generally expressed as a percentage by the following equation: % Hct-Bias=100%×(Gm−Gref)/Gref, where Gm is the determined glucose concentration at a specific hematocrit level and Gref is the reference glucose concentration at a reference hematocrit level. The larger the absolute value of the % Hct-bias, the more the hematocrit level of the sample (expressed as % Hct, the percentage of red blood cell volume/sample volume) is reducing the accuracy of the determined glucose concentration.
For example, if blood samples containing identical glucose concentrations, but having hematocrit levels of 20, 40, and 60%, are analyzed, three different glucose concentrations will be reported by a system based on one set of calibration constants (slope and intercept of the 40% hematocrit containing blood sample, for instance). Thus, even though the blood glucose concentrations are the same, the system will report that the 20% hematocrit sample contains more glucose than the 40% hematocrit sample, and that the 60% hematocrit sample contains less glucose than the 40% hematocrit sample. “Hematocrit sensitivity” is an expression of the degree to which changes in the hematocrit level of a sample affect the bias values for an analysis. Hematocrit sensitivity may be defined as the numerical values of the percent biases per percent hematocrit, thus bias/%-bias per % Hct.
Biosensor systems may provide an output signal during the analysis of the biological fluid including error from multiple error sources. These error sources contribute to the total error, which may be reflected in an abnormal output signal, such as when one or more portions or the entire output signal is non-responsive or improperly responsive to the analyte concentration of the sample.
The total error in the output signal may originate from one or more error contributors, such as the physical characteristics of the sample, the environmental aspects of the sample, the operating conditions of the system, the manufacturing variation between test sensor lots, and the like. Physical characteristics of the sample include hematocrit (red blood cell) concentration, interfering substances, such as lipids and proteins, and the like. Interfering substances include ascorbic acid, uric acid, acetaminophen, and the like. Environmental aspects of the sample include temperature, oxygen content of the air, and the like. Operating conditions of the system include underfill conditions when the sample size is not large enough, slow-filling of the sample, intermittent electrical contact between the sample and one or more electrodes in the test sensor, prior degradation of the reagents that interact with the analyte, and the like. Manufacturing variations between test sensor lots include changes in the amount and/or activity of the reagents, changes in the electrode area and/or spacing, changes in the electrical conductivity of the conductors and electrodes, and the like. A test sensor lot is preferably made in a single manufacturing run where lot-to-lot manufacturing variation is substantially reduced or eliminated. Manufacturing variations also may be introduced as the activity of the reagents changes or degrades between the time the test sensor is manufactured and when it is used for an analysis. There may be other contributors or a combination of error contributors that cause error in the analysis.
Percent bias, percent bias standard deviation, mean percent bias, relative error, and hematocrit sensitivity are independent ways to express the measurement performance of a biosensor system. Additional ways may be used to express the measurement performance of a biosensor system.
Percent bias is a representation of the accuracy of the biosensor system in relation to a reference analyte concentration, while the percent bias standard deviation reflects the accuracy of multiple analyses, with regard to error arising from the physical characteristics of the sample, the environmental aspects of the sample, and the operating conditions of the system. Thus, a decrease in percent bias standard deviation represents an increase in the measurement performance of the biosensor system across multiple analyses.
The mean may be determined for the percent biases determined from multiple analyses using test sensors from a single lot to provide a “mean percent bias” for the multiple analyses. The mean percent bias may be determined for a single lot of test sensors by using a subset of the lot, such as 100-140 test sensors, to analyze multiple blood samples.
Relative error is a general expression of error that may be expressed as ΔG/Gref (relative error)=(Gcalculated−Gref)/Gref=Gcalculated/Gref−1; where ΔG is the error present in the analysis determined analyte concentration in relation to the reference analyte concentration; Gcalculated is the analyte concentration determined from the sample during the analysis; and Gref is the analyte concentration of the sample as determined by a reference instrument.
Increasing the measurement performance of the biosensor system by reducing error from these or other sources means that more of the analyte concentrations determined by the biosensor system may be used for accurate therapy by the patient when blood glucose is being monitored, for example. Additionally, the need to discard test sensors and repeat the analysis by the patient also may be reduced.
A test case is a collection of multiple analyses (data population) arising under substantially the same testing conditions using test sensors from the same lot. For example, determined analyte concentration values have typically exhibited poorer measurement performance for user self-testing than for health care professional (“HCP”) testing and poorer measurement performance for HCP-testing than for controlled environment testing. This difference in measurement performance may be reflected in larger percent bias standard deviations for analyte concentrations determined through user self-testing than for analyte concentrations determined through HCP-testing or through controlled environment testing. A controlled environment is an environment where physical characteristics and environmental aspects of the sample may be controlled, preferably a laboratory setting. Thus, in a controlled environment, hematocrit concentrations can be fixed and actual sample temperatures can be known and compensated. In a HCP test case, the operating condition error may be reduced or eliminated. In a user self-testing test case, such as a clinical trial, the determined analyte concentrations likely will include error from all types of error sources.
Biosensor systems may have a single source of uncompensated output values responsive to a redox or light-based reaction of the analyte, such as the counter and working electrodes of an electrochemical system. Biosensor systems also may have the optional ability to determine or estimate temperature, such as with one or more thermocouples or other means. In addition to these systems, biosensor systems also may have the ability to generate additional output values external to those from the analyte or from a mediator responsive to the analyte. For example, in an electrochemical test sensor, one or more electrical conductors also may extend into the sample reservoir to provide functionality not provided by the working and counter electrodes. Such conductors may lack one or more of the working electrode reagents, such as the mediator, thus allowing for the subtraction of a background interferent signal from the working electrode signal.
Many biosensor systems include one or more methods to compensate for error associated with an analysis, thus attempting to improve the measurement performance of the biosensor system. Compensation methods may increase the measurement performance of a biosensor system by providing the biosensor system with the ability to compensate for inaccurate analyses, thus increasing the accuracy and/or precision of the concentration values obtained from the system. However, these methods have had difficulty compensating the analyte value obtained from substantially continuous output signals terminating in an end-point reading, which is correlated with the analyte concentration of the sample.
For many continuous processes, such as a Cottrell decay recorded from a relatively long duration potential input signal, the decay characteristics of the output signal may be described from existing theory with a decay constant. However, this constant may be less sensitive or insensitive to the physical characteristics of the sample or the operation conditions of the system.
One method of implementing error compensation is to use a gated input signal as opposed to substantially continuous input signal. In these gated or pulsed systems, the changes in the input signal perturbate the reaction of the sample so that compensation information may be obtained. However, for analysis systems using substantially continuous input signals to drive the reaction (commonly electrochemical coulometry or Cottrell-decay amperometry) and for analysis systems that observe a reaction that is started and observed until an end-point is reached (commonly optical), the compensation information as obtained from a perturbation of the reaction of the sample is unavailable. Even in a sample perturbated by a gated input signal, additional compensation information may be available during the continuous portions of the input signal, which may not otherwise be used by conventional error compensation techniques.
Accordingly, there is an ongoing need for improved biosensor systems, especially those that may provide increasingly accurate determination of sample analyte concentrations when an end-point reading from a substantially continuous output signal is correlated with the analyte concentration of the sample and/or when compensation information is unavailable from a perturbation of the reaction. The systems, devices, and methods of the present invention overcome at least one of the disadvantages associated with conventional biosensor systems.